A newer version of the Gradio SDK is available:
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metadata
title: MedGuard
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: true
license: mit
short_description: AI Medication Safety with Multi-Agent MCP
tags:
- healthcare
- mcp
- agents
- drug-interactions
- medication-safety
- hackathon
- pharmacogenomics
- clinical-decision-support
- langgraph
- multi-agent
- mcp-in-action-track-enterprise
- building-mcp-track-enterprise
🏥 MedGuard – AI-Powered Medication Safety Platform
Welcome to our submission for the Hugging Face GenAI Agents & MCP Hackathon!
This project showcases a production-grade multi-agent system powered by LangGraph and the Model Context Protocol (MCP), designed to analyze medication safety, detect dangerous drug interactions, and provide clinical decision support.
🎬 Live Demo Video | 🔗 GitHub Repository | 🤖 Claude Desktop Integration
X POST
🏆 Hackathon Tracks
| Track | Target | Status |
|---|---|---|
| Track 1: Building MCP | $10,000 | ✅ 10 MCP Tools + 3 Resources |
| Track 2: Consumer Use | $10,000 | ✅ Claude Desktop Integration |
| Track 3: Agentic Use | $10,000 | ✅ Multi-Agent LangGraph System |
| Blaxel Choice Award | $2,500 | ✅ Full Blaxel Platform Integration |
🚨 Why This Matters: The Problem We're Solving
🌐 Project Overview
MedGuard leverages 5 autonomous AI agents that collaborate to perform comprehensive medication safety analysis:
| Agent | Role | Key Features |
|---|---|---|
| 🔍 Drug Interaction Agent | Analyzes DDIs using knowledge graphs | CYP enzyme conflicts, PubMed enhancement, severity scoring |
| 👤 Personalization Agent | Patient-specific adjustments | Renal/hepatic dosing, pharmacogenomics, Beers Criteria |
| 📋 Guideline Agent | Clinical compliance checking | AHA/ACC, ADA, ESC guidelines with evidence levels |
| 💰 Cost Agent | Formulary optimization | Generic substitutions, therapeutic alternatives |
| 📝 Explanation Agent | Synthesis and communication | Prioritized recommendations, patient-friendly summaries |
🔄 LangGraph Orchestration Architecture
┌────────────────────────────────────────────────────────────────────────────┐
│ MedGuard Multi-Agent Orchestrator │
│ │
│ START │
│ │ │
│ ▼ │
│ ┌─────────────────────────────┐ │
│ │ Drug Interaction Agent │◄── Entry point (always runs first) │
│ │ • 25+ known DDIs │ │
│ │ • CYP enzyme analysis │ │
│ │ • ML severity prediction │ │
│ └───────────┬─────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────┐ │
│ │ CONDITIONAL ROUTER │◄── Severity-based intelligent routing │
│ │ Based on risk level │ │
│ └───────────┬─────────────────┘ │
│ │ │
│ ┌──────────┼──────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ "critical" "parallel" "low_risk" │
│ │ │ │ │
│ ▼ │ │ │
│ ┌────────┐ │ │ │
│ │ Human │ │ │ │
│ │ Review │ │ │ │
│ │ FLAG │ │ │ │
│ └───┬────┘ │ │ │
│ │ ┌────┴────┐ │ │
│ │ │Parallel │ │ │
│ │ │Execution│ │ │
│ │ └────┬────┘ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────────────────────┐ │
│ │ Personalization Agent │ │
│ │ Guideline Compliance Agent │◄── Run in parallel for efficiency │
│ │ Cost Optimization Agent │ │
│ └───────────┬─────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────┐ │
│ │ Explanation Agent │◄── Final synthesis & prioritization │
│ │ • Safety score (0-100) │ │
│ │ • Prioritized actions │ │
│ │ • Patient-friendly text │ │
│ └───────────┬─────────────────┘ │
│ │ │
│ ▼ │
│ END │
└────────────────────────────────────────────────────────────────────────────┘
🛠️ MCP Server Integration (10 Tools + 3 Resources)
MCP Tools
| # | Tool | Description | Parameters |
|---|---|---|---|
| 1 | analyze_medication_safety |
Full 5-agent pipeline | patient_id, query |
| 2 | check_drug_interactions |
DDI detection via Neo4j | medications[], allergies[] |
| 3 | get_personalized_dosing |
Patient-specific dosing | patient_id, medication, indication |
| 4 | check_guideline_compliance |
Clinical guideline check | patient_id, condition |
| 5 | optimize_medication_costs |
Generic alternatives | medications[], insurance_type |
| 6 | get_patient_profile |
Demographics & history | patient_id |
| 7 | search_clinical_guidelines |
BioBERT vector search | query, limit |
| 8 | explain_medication_decision |
Patient-friendly text | analysis, reading_level |
| 9 | search_pubmed_literature |
PubMed via MCP Search | query, study_types[] |
| 10 | search_fda_safety_alerts |
FDA alerts via MCP Search | drug_name, years |
MCP Resources
| URI | Description |
|---|---|
guidelines://clinical-practice |
Clinical practice guidelines database |
database://drug-interactions |
Drug interaction knowledge graph |
alerts://fda-safety |
FDA safety communications |
🗂️ Data Sources & Medical Knowledge Bases
Our platform uses real, authoritative, evidence-based medical data sources:
1. 💊 DrugBank – Drug Interaction Database
- Purpose: Curated drug-drug interactions with mechanisms and management
- Coverage: 25+ high-risk interaction pairs (expandable to 3,000+)
- Data: Severity, mechanism, clinical effect, management strategy, evidence level
- Usage: Core DDI detection for all medication safety analysis
2. 🧬 Pharmacogenomics Database (PharmGKB)
- Purpose: Genetic variants affecting drug metabolism
- Enzymes Covered: CYP2D6, CYP2C9, CYP2C19, CYP3A4, CYP1A2
- Phenotypes: Poor metabolizer, intermediate, normal, ultrarapid
- Usage: Personalized dosing recommendations based on genetic markers
3. 👴 AGS Beers Criteria (2023)
- Purpose: Potentially inappropriate medications in older adults
- Coverage: 30+ medication classes to avoid in elderly
- Source: American Geriatrics Society
- Usage: Automatic flagging for patients ≥65 years
4. 📋 Clinical Practice Guidelines
| Organization | Guidelines Included |
|---|---|
| AHA/ACC | Heart Failure, AFib, CAD, Hypertension |
| ADA | Type 2 Diabetes Standards of Care 2024 |
| ESC | European cardiovascular guidelines |
| KDIGO | Chronic Kidney Disease 2024 |
5. 🔬 PubMed/MEDLINE (via MCP Search)
- Purpose: Literature search for clinical evidence
- API: MCP Search protocol integration
- Usage: Enhance recommendations with recent research citations
6. ⚠️ FDA Safety Communications
- Purpose: Drug safety alerts, recalls, black box warnings
- API: MCP Search protocol integration
- Usage: Real-time safety alert checking
📦 Technology Stack
| Layer | Technology | Purpose |
|---|---|---|
| MCP Server | Python mcp SDK |
10 tools, 3 resources, stdio transport |
| Orchestration | LangGraph StateGraph | Conditional routing, parallel execution |
| LLM | Claude 4 Sonnet / GPT-4o / Gemini 2.0 | Medical analysis, synthesis |
| Knowledge Graph | Neo4j | Drug interaction network |
| Vector Search | Qdrant + BioBERT | Semantic guideline search |
| API | FastAPI | REST endpoints, HIPAA audit logging |
| Frontend | Gradio | Interactive demo UI |
| Databases | PostgreSQL, Redis | Patient data, session management |
| Cloud | Blaxel Platform | Serverless deployment, observability |
🧩 Core Components
🔍 Drug Interaction Agent (drug_interaction_agent_enhanced.py)
- Role: Primary safety analysis entry point
- Capabilities:
- Known interaction database lookup (DrugBank)
- CYP enzyme metabolic conflict detection
- ML-based novel interaction prediction
- PubMed literature enhancement
- Severity classification (minor → moderate → major → critical)
👤 Personalization Agent (personalization_agent.py)
- Role: Patient-specific safety adjustments
- Capabilities:
- Renal dose adjustments (eGFR-based)
- Hepatic impairment considerations
- Pharmacogenomic analysis (CYP variants)
- Beers Criteria screening (age ≥65)
- Polypharmacy detection (5+/10+ meds)
📋 Guideline Compliance Agent (guideline_compliance_agent.py)
- Role: Evidence-based standard verification
- Capabilities:
- Condition-specific therapy checks
- Missing therapy identification
- Guideline citation with evidence levels
- Therapeutic class mapping
💰 Cost Optimization Agent (cost_optimization_agent.py)
- Role: Formulary and cost efficiency
- Capabilities:
- Brand → generic substitution
- Therapeutic class alternatives
- Insurance formulary optimization
- Annual savings calculation
📝 Explanation Agent (explanation_agent.py)
- Role: Clinical synthesis and communication
- Capabilities:
- Safety score calculation (0-100)
- Prioritized recommendation list
- Executive summary for clinicians
- Patient-friendly explanations (adjustable reading level)
🧑⚕️ Demo Patients
| ID | Patient | Age | Key Demonstration |
|---|---|---|---|
| P001 | John Smith | 67 | Warfarin + Aspirin (major bleeding risk), CKD Stage 3, CYP2C9*3 |
| P002 | Maria Garcia | 45 | Sertraline + Tramadol (serotonin syndrome risk) |
| P003 | Robert Chen | 72 | 8 medications, hyperkalemia risk, HF + COPD + CKD |
| P004 | Sarah Johnson | 55 | Simvastatin + Amlodipine (CYP3A4 interaction, myopathy risk) |
| P005 | James Wilson | 78 | 6 Beers Criteria violations, CYP2D6 poor metabolizer |
🚀 Deployment Options
Option 1: Hugging Face Spaces (This Demo)
# Already deployed! Just use the Gradio interface above
Option 2: Claude Desktop Integration
{
"mcpServers": {
"healthcare-multi-agent": {
"command": "python",
"args": ["-m", "src.mcp.healthcare_mcp_server"],
"cwd": "/path/to/mcp1stbirthday_hack"
}
}
}
Option 3: Blaxel Platform
cd my-agent && bl deploy
bl run agent healthcare-multi-agent-system --data '{"inputs": "Analyze patient P001"}'
Option 4: Docker Compose
docker-compose up -d
# API: http://localhost:8000
# UI: http://localhost:7860
📊 Example Analysis Output
🏥 MedGuard Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Patient: John Smith (P001) | Age: 67 | Medications: 4
⚠️ SAFETY SCORE: 55/100 (MODERATE RISK)
⚠️ REQUIRES CLINICAL REVIEW
━━━ CRITICAL FINDINGS ━━━
🔴 MAJOR DRUG INTERACTION: Warfarin + Aspirin
Mechanism: Additive antiplatelet/anticoagulant effects
Effect: Significantly increased bleeding risk (GI, intracranial)
Management: Monitor INR closely, add PPI, use 81mg aspirin only
Evidence: Established (PMID: 27432982)
🟠 RENAL ADJUSTMENT NEEDED: Metformin
eGFR: 58 mL/min (threshold: 30)
Action: Monitor renal function; avoid if eGFR <30
🟠 PHARMACOGENOMIC ALERT: Warfarin + CYP2C9*3
Phenotype: Intermediate metabolizer
Action: May require 20-30% lower warfarin dose
━━━ RECOMMENDATIONS ━━━
1. [CRITICAL] Review warfarin + aspirin combination
2. [HIGH] Add PPI for gastroprotection
3. [MODERATE] Consider CYP2C9 genotype-guided dosing
4. [LOW] Generic substitution available: Save $285/month
🧑💻 Authors
MedGuard Team – MCP 1st Birthday Hackathon Submission
- Built with ❤️ for patient safety
- Leveraging state-of-the-art AI agent orchestration
- Production-ready architecture for healthcare applications
📝 License
This project is licensed under the MIT License – see LICENSE for details.
🙏 Acknowledgments
- Anthropic for Claude and the MCP protocol
- Hugging Face for hosting and the hackathon
- Blaxel for serverless AI infrastructure
- DrugBank, PharmGKB, AGS, AHA/ACC/ADA for medical knowledge
- The healthcare AI community for inspiration
Built for the MCP 1st Birthday Hackathon
Making medication safety accessible through AI agents
🏥 💊 🤖 🔬 💚